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ChatGPT:
The article outlines a six-step workflow for using ChatGPT as a risk detection tool: (1) collecting real-time market, on-chain, and textual data; (2) cleaning and labeling it; (3) synthesizing it into structured summaries; (4) assigning risk levels; (5) verifying outputs with trusted data sources; and (6) refining signals after volatility events. This structured process turns scattered information into a daily risk map.
ChatGPT’s key strengths include synthesizing massive data, detecting shifts in crowd psychology, and recognizing complex stress patterns—such as high leverage plus negative sentiment plus thinning liquidity. However, it remains probabilistic, not predictive: its insights depend on timely, accurate data and cannot anticipate unprecedented macro shocks or exchange microstructure failures.
Had this AI-driven workflow been running before the October crash, it likely would have raised its risk level to “Alert” due to excessive leverage, rising volatility, and worsening sentiment. Still, the model would not have predicted the exact crash date. Ultimately, ChatGPT serves as an advanced “risk radar,” enhancing trader awareness and discipline—but not as a crystal ball for market timing.
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